Indexed by:期刊论文

Date of Publication:2022-06-28

Journal:化工学报

Volume:70

Issue:12

Page Number:4722-4729

ISSN No.:0438-1157

Abstract:The business of fragrances has become a multibillion-dollar market, and the development of fragrance tuned technology enriches modern social life. In this study, the inverse machine learning model for fragrance tuned design is proposed. The molecular surface charge density distribution based on the conductor-like screening model (COSMO) is used as the structural descriptor of the fragrance molecule to design the final fragrance tuned product. First, the fragrance attributes are identified and transform attributes into target properties according to needs. Then, change odor scores and establish the Inverse Machine Learning (IML) models, in which the input variables are odors and the output variable is molecular structure descriptor. Based on the trained IML models, the structure descriptors of the potential product are predicted according to the target properties. Finally, the candidate tuned mixtures were screened out using Euclidean-based method in the specified database. In this paper, two types of fragrant examples are taken as examples. The framework is used to design the fragrance, and the experimental data and odor radar map are used to verify the experimental results.

Note:新增回溯数据